LAPSE:2023.24777
Published Article

LAPSE:2023.24777
Hybrid Models for Efficient Control, Optimization, and Monitoring of Thermo-Chemical Processes and Plants
March 28, 2023
Abstract
This paper describes a procedure and an IT product that combine numerical models, expert knowledge, and data-based models through artificial intelligence (AI)-based hybrid models to enable the integrated control, optimization, and monitoring of processes and plants. The working principle of the hybrid model is demonstrated by NOx reduction through guided oscillating combustion at the pulverized fuel boiler pilot incineration plant at the Institute for Technical Chemistry, Karlsruhe Institute of Technology. The presented example refers to coal firing, but the approach can be easily applied to any other type of nitrogen-containing solid fuel. The need for a reduction in operation and maintenance costs for biomass-fired plants is huge, especially in the frame of emission reductions and, in the case of Germany, the potential loss of funding as a result of the Renewable Energy Law (Erneuerbare-Energien-Gesetz) for plants older than 20 years. Other social aspects, such as the departure of experienced personnel may be another reason for the increasing demand for data mining and the use of artificial intelligence (AI).
This paper describes a procedure and an IT product that combine numerical models, expert knowledge, and data-based models through artificial intelligence (AI)-based hybrid models to enable the integrated control, optimization, and monitoring of processes and plants. The working principle of the hybrid model is demonstrated by NOx reduction through guided oscillating combustion at the pulverized fuel boiler pilot incineration plant at the Institute for Technical Chemistry, Karlsruhe Institute of Technology. The presented example refers to coal firing, but the approach can be easily applied to any other type of nitrogen-containing solid fuel. The need for a reduction in operation and maintenance costs for biomass-fired plants is huge, especially in the frame of emission reductions and, in the case of Germany, the potential loss of funding as a result of the Renewable Energy Law (Erneuerbare-Energien-Gesetz) for plants older than 20 years. Other social aspects, such as the departure of experienced personnel may be another reason for the increasing demand for data mining and the use of artificial intelligence (AI).
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Keywords
artificial intelligence (AI), NOx reduction, numerical model, oscillating combustion
Subject
Suggested Citation
Freudenmann T, Gehrmann HJ, Aleksandrov K, El-Haji M, Stapf D. Hybrid Models for Efficient Control, Optimization, and Monitoring of Thermo-Chemical Processes and Plants. (2023). LAPSE:2023.24777
Author Affiliations
Freudenmann T: EDI GmbH—Engineering Data Intelligence, Wöschbacher Str. 73, 76327 Pfinztal-Berghausen, Germany
Gehrmann HJ: Institute for Technical Chemistry, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany [ORCID]
Aleksandrov K: Institute for Technical Chemistry, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
El-Haji M: EDI GmbH—Engineering Data Intelligence, Wöschbacher Str. 73, 76327 Pfinztal-Berghausen, Germany
Stapf D: Institute for Technical Chemistry, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
Gehrmann HJ: Institute for Technical Chemistry, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany [ORCID]
Aleksandrov K: Institute for Technical Chemistry, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
El-Haji M: EDI GmbH—Engineering Data Intelligence, Wöschbacher Str. 73, 76327 Pfinztal-Berghausen, Germany
Stapf D: Institute for Technical Chemistry, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
Journal Name
Processes
Volume
9
Issue
3
First Page
515
Year
2021
Publication Date
2021-03-12
ISSN
2227-9717
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Original Submission
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PII: pr9030515, Publication Type: Journal Article
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LAPSE:2023.24777
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https://doi.org/10.3390/pr9030515
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Mar 28, 2023
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